Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations2099836
Missing cells549
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 GiB
Average record size in memory813.6 B

Variable types

DateTime1
Numeric7
Text6
Categorical4

Alerts

alineación con portafolio estratégico is highly overall correlated with valorHigh correlation
cantidad is highly overall correlated with precioHigh correlation
categoria is highly overall correlated with categoria_macroHigh correlation
categoria_macro is highly overall correlated with categoriaHigh correlation
id is highly overall correlated with pedidoHigh correlation
pedido is highly overall correlated with idHigh correlation
precio is highly overall correlated with cantidadHigh correlation
valor is highly overall correlated with alineación con portafolio estratégicoHigh correlation
cantidad is highly skewed (γ1 = 377.7159296) Skewed
precio is highly skewed (γ1 = 75.62012249) Skewed
valor is highly skewed (γ1 = 86.3526775) Skewed
alineación con portafolio estratégico is highly skewed (γ1 = -623.4425064) Skewed

Reproduction

Analysis started2025-03-22 00:39:03.776530
Analysis finished2025-03-22 00:40:42.142737
Duration1 minute and 38.37 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

fecha
Date

Distinct756
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.0 MiB
Minimum1971-01-02 00:00:00
Maximum1973-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-21T19:40:42.235820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:42.343641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pedido
Real number (ℝ)

High correlation 

Distinct933935
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467009.18
Minimum2
Maximum933936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:42.443946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile46399
Q1231701
median466824
Q3702678.25
95-th percentile887651
Maximum933936
Range933934
Interquartile range (IQR)470977.25

Descriptive statistics

Standard deviation270549.12
Coefficient of variation (CV)0.57932291
Kurtosis-1.2073259
Mean467009.18
Median Absolute Deviation (MAD)235498
Skewness-0.0012157426
Sum9.806427 × 1011
Variance7.3196826 × 1010
MonotonicityNot monotonic
2025-03-21T19:40:42.541523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
771273 78
 
< 0.1%
57130 57
 
< 0.1%
662366 48
 
< 0.1%
478732 44
 
< 0.1%
173770 44
 
< 0.1%
632112 41
 
< 0.1%
65592 40
 
< 0.1%
145891 40
 
< 0.1%
805154 40
 
< 0.1%
558223 40
 
< 0.1%
Other values (933925) 2099364
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
933936 2
< 0.1%
933935 1
 
< 0.1%
933934 1
 
< 0.1%
933933 3
< 0.1%
933932 1
 
< 0.1%
933931 1
 
< 0.1%
933930 1
 
< 0.1%
933929 2
< 0.1%
933928 2
< 0.1%
933927 4
< 0.1%

id
Real number (ℝ)

High correlation 

Distinct419226
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165352
Minimum1
Maximum419226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:42.697991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8980
Q158294
median137973
Q3261744.25
95-th percentile384371.25
Maximum419226
Range419225
Interquartile range (IQR)203450.25

Descriptive statistics

Standard deviation121459.27
Coefficient of variation (CV)0.73454977
Kurtosis-1.0263245
Mean165352
Median Absolute Deviation (MAD)94770
Skewness0.45491189
Sum3.4721208 × 1011
Variance1.4752355 × 1010
MonotonicityNot monotonic
2025-03-21T19:40:42.797476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3037 1743
 
0.1%
1236 1682
 
0.1%
2906 1565
 
0.1%
3121 1366
 
0.1%
3357 1321
 
0.1%
2412 1173
 
0.1%
30822 998
 
< 0.1%
4206 892
 
< 0.1%
512 650
 
< 0.1%
3038 461
 
< 0.1%
Other values (419216) 2087985
99.4%
ValueCountFrequency (%)
1 85
< 0.1%
2 18
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 5
 
< 0.1%
6 36
< 0.1%
7 5
 
< 0.1%
8 9
 
< 0.1%
9 23
 
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
419226 2
 
< 0.1%
419225 1
 
< 0.1%
419224 1
 
< 0.1%
419223 1
 
< 0.1%
419222 1
 
< 0.1%
419221 11
< 0.1%
419220 8
< 0.1%
419219 1
 
< 0.1%
419218 6
< 0.1%
419217 2
 
< 0.1%

edad
Real number (ℝ)

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.870732
Minimum18
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:42.898238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile29
Q132
median43
Q349
95-th percentile58
Maximum67
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.5630711
Coefficient of variation (CV)0.22839513
Kurtosis-0.92834379
Mean41.870732
Median Absolute Deviation (MAD)8
Skewness0.17185007
Sum87921670
Variance91.452329
MonotonicityNot monotonic
2025-03-21T19:40:42.998219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 332758
 
15.8%
43 110451
 
5.3%
47 102710
 
4.9%
41 98523
 
4.7%
45 93176
 
4.4%
50 78762
 
3.8%
46 78428
 
3.7%
30 74883
 
3.6%
42 74559
 
3.6%
40 70853
 
3.4%
Other values (40) 984733
46.9%
ValueCountFrequency (%)
18 11
 
< 0.1%
19 240
 
< 0.1%
20 216
 
< 0.1%
21 569
 
< 0.1%
22 4143
 
0.2%
23 7722
0.4%
24 5801
 
0.3%
25 7831
0.4%
26 12352
0.6%
27 17506
0.8%
ValueCountFrequency (%)
67 294
 
< 0.1%
66 22
 
< 0.1%
65 90
 
< 0.1%
64 20375
1.0%
63 2055
 
0.1%
62 6806
 
0.3%
61 5553
 
0.3%
60 30346
1.4%
59 25744
1.2%
58 30632
1.5%
Distinct808
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.7 MiB
2025-03-21T19:40:43.214772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length27
Median length26
Mean length8.257823
Min length3

Characters and Unicode

Total characters17340074
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowEL CARMEN DE CHUCURI
2nd rowVILLANUEVA
3rd rowVILLANUEVA
4th rowVILLANUEVA
5th rowARROYOHONDO
ValueCountFrequency (%)
curiti 576380
23.0%
natagaima 344983
 
13.7%
villanueva 142303
 
5.7%
guatica 130383
 
5.2%
girardota 76950
 
3.1%
de 73799
 
2.9%
santa 53719
 
2.1%
don 41577
 
1.7%
matias 41577
 
1.7%
la 41484
 
1.7%
Other values (809) 986380
39.3%
2025-03-21T19:40:43.676274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3487137
20.1%
I 2314198
13.3%
T 1510427
8.7%
R 1254442
 
7.2%
U 1172303
 
6.8%
C 1074410
 
6.2%
N 993913
 
5.7%
E 756228
 
4.4%
G 702421
 
4.1%
O 647547
 
3.7%
Other values (28) 3427048
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16929904
97.6%
Space Separator 409699
 
2.4%
Other Punctuation 276
 
< 0.1%
Lowercase Letter 164
 
< 0.1%
Decimal Number 21
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3487137
20.6%
I 2314198
13.7%
T 1510427
8.9%
R 1254442
 
7.4%
U 1172303
 
6.9%
C 1074410
 
6.3%
N 993913
 
5.9%
E 756228
 
4.5%
G 702421
 
4.1%
O 647547
 
3.8%
Other values (14) 3016878
17.8%
Lowercase Letter
ValueCountFrequency (%)
a 41
25.0%
d 36
22.0%
o 23
14.0%
c 23
14.0%
l 18
11.0%
i 18
11.0%
í 5
 
3.0%
Decimal Number
ValueCountFrequency (%)
7 7
33.3%
3 7
33.3%
6 7
33.3%
Space Separator
ValueCountFrequency (%)
409699
100.0%
Other Punctuation
ValueCountFrequency (%)
. 276
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16930068
97.6%
Common 410006
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3487137
20.6%
I 2314198
13.7%
T 1510427
8.9%
R 1254442
 
7.4%
U 1172303
 
6.9%
C 1074410
 
6.3%
N 993913
 
5.9%
E 756228
 
4.5%
G 702421
 
4.1%
O 647547
 
3.8%
Other values (21) 3017042
17.8%
Common
ValueCountFrequency (%)
409699
99.9%
. 276
 
0.1%
7 7
 
< 0.1%
3 7
 
< 0.1%
6 7
 
< 0.1%
( 5
 
< 0.1%
) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17340069
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3487137
20.1%
I 2314198
13.3%
T 1510427
8.7%
R 1254442
 
7.2%
U 1172303
 
6.8%
C 1074410
 
6.2%
N 993913
 
5.7%
E 756228
 
4.4%
G 702421
 
4.1%
O 647547
 
3.7%
Other values (27) 3427043
19.8%
None
ValueCountFrequency (%)
í 5
100.0%

zona
Categorical

Distinct34
Distinct (%)< 0.1%
Missing18
Missing (%)< 0.1%
Memory size133.2 MiB
SANTANDER
739964 
TOLIMA
407991 
ANTIOQUIA
224114 
LA GUAJIRA
145807 
RISARALDA
132904 
Other values (29)
449038 

Length

Max length15
Median length9
Mean length8.402673
Min length4

Characters and Unicode

Total characters17644084
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSANTANDER
2nd rowLA GUAJIRA
3rd rowLA GUAJIRA
4th rowLA GUAJIRA
5th rowBOLÍVAR

Common Values

ValueCountFrequency (%)
SANTANDER 739964
35.2%
TOLIMA 407991
19.4%
ANTIOQUIA 224114
 
10.7%
LA GUAJIRA 145807
 
6.9%
RISARALDA 132904
 
6.3%
CUNDINAMARCA 102305
 
4.9%
NORTE SANTANDER 59017
 
2.8%
BOYACA 45454
 
2.2%
HUILA 36290
 
1.7%
META 32602
 
1.6%
Other values (24) 173370
 
8.3%

Length

2025-03-21T19:40:43.778488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santander 798981
34.7%
tolima 407991
17.7%
antioquia 224114
 
9.7%
la 145807
 
6.3%
guajira 145807
 
6.3%
risaralda 132904
 
5.8%
cundinamarca 102305
 
4.4%
norte 59017
 
2.6%
boyaca 45454
 
2.0%
huila 36290
 
1.6%
Other values (26) 206089
 
8.9%

Most occurring characters

ValueCountFrequency (%)
A 4008766
22.7%
N 2169725
12.3%
T 1583306
 
9.0%
R 1447066
 
8.2%
I 1330627
 
7.5%
D 1054626
 
6.0%
S 972829
 
5.5%
E 956562
 
5.4%
L 837651
 
4.7%
O 822694
 
4.7%
Other values (24) 2460232
13.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17439113
98.8%
Space Separator 204941
 
1.2%
Lowercase Letter 20
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4008766
23.0%
N 2169725
12.4%
T 1583306
 
9.1%
R 1447066
 
8.3%
I 1330627
 
7.6%
D 1054626
 
6.0%
S 972829
 
5.6%
E 956562
 
5.5%
L 837651
 
4.8%
O 822694
 
4.7%
Other values (17) 2255261
12.9%
Lowercase Letter
ValueCountFrequency (%)
a 5
25.0%
c 5
25.0%
í 5
25.0%
o 5
25.0%
Space Separator
ValueCountFrequency (%)
204941
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17439133
98.8%
Common 204951
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4008766
23.0%
N 2169725
12.4%
T 1583306
 
9.1%
R 1447066
 
8.3%
I 1330627
 
7.6%
D 1054626
 
6.0%
S 972829
 
5.6%
E 956562
 
5.5%
L 837651
 
4.8%
O 822694
 
4.7%
Other values (21) 2255281
12.9%
Common
ValueCountFrequency (%)
204941
> 99.9%
( 5
 
< 0.1%
) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17573099
99.6%
None 70985
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4008766
22.8%
N 2169725
12.3%
T 1583306
 
9.0%
R 1447066
 
8.2%
I 1330627
 
7.6%
D 1054626
 
6.0%
S 972829
 
5.5%
E 956562
 
5.4%
L 837651
 
4.8%
O 822694
 
4.7%
Other values (18) 2389247
13.6%
None
ValueCountFrequency (%)
Á 29632
41.7%
Ñ 19021
26.8%
Í 17924
25.3%
Ó 4286
 
6.0%
É 117
 
0.2%
í 5
 
< 0.1%

asesor
Text

Distinct608
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size133.4 MiB
2025-03-21T19:40:43.964877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6363716
Min length8

Characters and Unicode

Total characters20234800
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowasesor_2
2nd rowasesor_3
3rd rowasesor_4
4th rowasesor_5
5th rowasesor_6
ValueCountFrequency (%)
asesor_137 14230
 
0.7%
asesor_7 14230
 
0.7%
asesor_45 13216
 
0.6%
asesor_256 13000
 
0.6%
asesor_170 12804
 
0.6%
asesor_165 12379
 
0.6%
asesor_13 12254
 
0.6%
asesor_139 12078
 
0.6%
asesor_149 11977
 
0.6%
asesor_6 11877
 
0.6%
Other values (598) 1971791
93.9%
2025-03-21T19:40:44.296351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 4199672
20.8%
a 2099836
10.4%
e 2099836
10.4%
o 2099836
10.4%
r 2099836
10.4%
_ 2099836
10.4%
1 1121748
 
5.5%
2 871054
 
4.3%
3 639767
 
3.2%
4 491297
 
2.4%
Other values (6) 2412082
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12599016
62.3%
Decimal Number 5535948
27.4%
Connector Punctuation 2099836
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1121748
20.3%
2 871054
15.7%
3 639767
11.6%
4 491297
8.9%
7 458887
8.3%
5 439879
 
7.9%
6 417157
 
7.5%
8 404211
 
7.3%
9 376879
 
6.8%
0 315069
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
s 4199672
33.3%
a 2099836
16.7%
e 2099836
16.7%
o 2099836
16.7%
r 2099836
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 2099836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12599016
62.3%
Common 7635784
37.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2099836
27.5%
1 1121748
14.7%
2 871054
11.4%
3 639767
 
8.4%
4 491297
 
6.4%
7 458887
 
6.0%
5 439879
 
5.8%
6 417157
 
5.5%
8 404211
 
5.3%
9 376879
 
4.9%
Latin
ValueCountFrequency (%)
s 4199672
33.3%
a 2099836
16.7%
e 2099836
16.7%
o 2099836
16.7%
r 2099836
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20234800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4199672
20.8%
a 2099836
10.4%
e 2099836
10.4%
o 2099836
10.4%
r 2099836
10.4%
_ 2099836
10.4%
1 1121748
 
5.5%
2 871054
 
4.3%
3 639767
 
3.2%
4 491297
 
2.4%
Other values (6) 2412082
11.9%
Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
2025-03-21T19:40:44.443637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.631986
Min length13

Characters and Unicode

Total characters28624935
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpunto_venta_2
2nd rowpunto_venta_2
3rd rowpunto_venta_2
4th rowpunto_venta_3
5th rowpunto_venta_4
ValueCountFrequency (%)
punto_venta_7 132386
 
6.3%
punto_venta_4 123307
 
5.9%
punto_venta_2 118419
 
5.6%
punto_venta_6 116560
 
5.6%
punto_venta_10 106348
 
5.1%
punto_venta_9 101083
 
4.8%
punto_venta_21 97357
 
4.6%
punto_venta_1 73464
 
3.5%
punto_venta_34 58383
 
2.8%
punto_venta_20 56943
 
2.7%
Other values (56) 1115586
53.1%
2025-03-21T19:40:44.660331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 4199672
14.7%
t 4199672
14.7%
_ 4199672
14.7%
p 2099836
7.3%
o 2099836
7.3%
v 2099836
7.3%
e 2099836
7.3%
a 2099836
7.3%
u 2099836
7.3%
2 735466
 
2.6%
Other values (9) 2691437
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20998360
73.4%
Connector Punctuation 4199672
 
14.7%
Decimal Number 3426903
 
12.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 735466
21.5%
1 674433
19.7%
3 550932
16.1%
4 299012
8.7%
7 261590
 
7.6%
0 207129
 
6.0%
6 203344
 
5.9%
5 191679
 
5.6%
9 168879
 
4.9%
8 134439
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
n 4199672
20.0%
t 4199672
20.0%
p 2099836
10.0%
o 2099836
10.0%
v 2099836
10.0%
e 2099836
10.0%
a 2099836
10.0%
u 2099836
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4199672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20998360
73.4%
Common 7626575
 
26.6%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 4199672
55.1%
2 735466
 
9.6%
1 674433
 
8.8%
3 550932
 
7.2%
4 299012
 
3.9%
7 261590
 
3.4%
0 207129
 
2.7%
6 203344
 
2.7%
5 191679
 
2.5%
9 168879
 
2.2%
Latin
ValueCountFrequency (%)
n 4199672
20.0%
t 4199672
20.0%
p 2099836
10.0%
o 2099836
10.0%
v 2099836
10.0%
e 2099836
10.0%
a 2099836
10.0%
u 2099836
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28624935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4199672
14.7%
t 4199672
14.7%
_ 4199672
14.7%
p 2099836
7.3%
o 2099836
7.3%
v 2099836
7.3%
e 2099836
7.3%
a 2099836
7.3%
u 2099836
7.3%
2 735466
 
2.6%
Other values (9) 2691437
9.4%

cluster
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.2 MiB
cluster_tienda_3
926405 
cluster_tienda_2
839005 
cluster_tienda_1
207099 
cluster_tienda_4
98458 
cluster_tienda_5
 
26109
Other values (4)
 
2760

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters33597376
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcluster_tienda_2
2nd rowcluster_tienda_2
3rd rowcluster_tienda_2
4th rowcluster_tienda_3
5th rowcluster_tienda_2

Common Values

ValueCountFrequency (%)
cluster_tienda_3 926405
44.1%
cluster_tienda_2 839005
40.0%
cluster_tienda_1 207099
 
9.9%
cluster_tienda_4 98458
 
4.7%
cluster_tienda_5 26109
 
1.2%
cluster_tienda_6 1216
 
0.1%
cluster_tienda_8 791
 
< 0.1%
cluster_tienda_7 752
 
< 0.1%
cluster_tienda_9 1
 
< 0.1%

Length

2025-03-21T19:40:44.744433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T19:40:44.845589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
cluster_tienda_3 926405
44.1%
cluster_tienda_2 839005
40.0%
cluster_tienda_1 207099
 
9.9%
cluster_tienda_4 98458
 
4.7%
cluster_tienda_5 26109
 
1.2%
cluster_tienda_6 1216
 
0.1%
cluster_tienda_8 791
 
< 0.1%
cluster_tienda_7 752
 
< 0.1%
cluster_tienda_9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 4199672
12.5%
e 4199672
12.5%
_ 4199672
12.5%
c 2099836
 
6.2%
i 2099836
 
6.2%
a 2099836
 
6.2%
l 2099836
 
6.2%
n 2099836
 
6.2%
d 2099836
 
6.2%
r 2099836
 
6.2%
Other values (11) 6299508
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27297868
81.2%
Connector Punctuation 4199672
 
12.5%
Decimal Number 2099836
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4199672
15.4%
e 4199672
15.4%
c 2099836
7.7%
i 2099836
7.7%
a 2099836
7.7%
l 2099836
7.7%
n 2099836
7.7%
d 2099836
7.7%
r 2099836
7.7%
s 2099836
7.7%
Decimal Number
ValueCountFrequency (%)
3 926405
44.1%
2 839005
40.0%
1 207099
 
9.9%
4 98458
 
4.7%
5 26109
 
1.2%
6 1216
 
0.1%
8 791
 
< 0.1%
7 752
 
< 0.1%
9 1
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 4199672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27297868
81.2%
Common 6299508
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4199672
15.4%
e 4199672
15.4%
c 2099836
7.7%
i 2099836
7.7%
a 2099836
7.7%
l 2099836
7.7%
n 2099836
7.7%
d 2099836
7.7%
r 2099836
7.7%
s 2099836
7.7%
Common
ValueCountFrequency (%)
_ 4199672
66.7%
3 926405
 
14.7%
2 839005
 
13.3%
1 207099
 
3.3%
4 98458
 
1.6%
5 26109
 
0.4%
6 1216
 
< 0.1%
8 791
 
< 0.1%
7 752
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33597376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4199672
12.5%
e 4199672
12.5%
_ 4199672
12.5%
c 2099836
 
6.2%
i 2099836
 
6.2%
a 2099836
 
6.2%
l 2099836
 
6.2%
n 2099836
 
6.2%
d 2099836
 
6.2%
r 2099836
 
6.2%
Other values (11) 6299508
18.8%

categoria_macro
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.2 MiB
categoria_macro_2
1304967 
categoria_macro_4
502419 
categoria_macro_1
187124 
categoria_macro_3
 
96038
categoria_macro_5
 
9288

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters35697212
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcategoria_macro_1
2nd rowcategoria_macro_2
3rd rowcategoria_macro_3
4th rowcategoria_macro_2
5th rowcategoria_macro_4

Common Values

ValueCountFrequency (%)
categoria_macro_2 1304967
62.1%
categoria_macro_4 502419
 
23.9%
categoria_macro_1 187124
 
8.9%
categoria_macro_3 96038
 
4.6%
categoria_macro_5 9288
 
0.4%

Length

2025-03-21T19:40:44.944020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T19:40:45.045002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
categoria_macro_2 1304967
62.1%
categoria_macro_4 502419
 
23.9%
categoria_macro_1 187124
 
8.9%
categoria_macro_3 96038
 
4.6%
categoria_macro_5 9288
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 6299508
17.6%
c 4199672
11.8%
o 4199672
11.8%
r 4199672
11.8%
_ 4199672
11.8%
t 2099836
 
5.9%
e 2099836
 
5.9%
g 2099836
 
5.9%
i 2099836
 
5.9%
m 2099836
 
5.9%
Other values (5) 2099836
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29397704
82.4%
Connector Punctuation 4199672
 
11.8%
Decimal Number 2099836
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6299508
21.4%
c 4199672
14.3%
o 4199672
14.3%
r 4199672
14.3%
t 2099836
 
7.1%
e 2099836
 
7.1%
g 2099836
 
7.1%
i 2099836
 
7.1%
m 2099836
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 1304967
62.1%
4 502419
 
23.9%
1 187124
 
8.9%
3 96038
 
4.6%
5 9288
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 4199672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29397704
82.4%
Common 6299508
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6299508
21.4%
c 4199672
14.3%
o 4199672
14.3%
r 4199672
14.3%
t 2099836
 
7.1%
e 2099836
 
7.1%
g 2099836
 
7.1%
i 2099836
 
7.1%
m 2099836
 
7.1%
Common
ValueCountFrequency (%)
_ 4199672
66.7%
2 1304967
 
20.7%
4 502419
 
8.0%
1 187124
 
3.0%
3 96038
 
1.5%
5 9288
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35697212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6299508
17.6%
c 4199672
11.8%
o 4199672
11.8%
r 4199672
11.8%
_ 4199672
11.8%
t 2099836
 
5.9%
e 2099836
 
5.9%
g 2099836
 
5.9%
i 2099836
 
5.9%
m 2099836
 
5.9%
Other values (5) 2099836
 
5.9%

categoria
Categorical

High correlation 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.6 MiB
categoria_3
459528 
categoria_7
435661 
categoria_5
288953 
categoria_11
156428 
categoria_1
149293 
Other values (22)
609973 

Length

Max length12
Median length11
Mean length11.213215
Min length11

Characters and Unicode

Total characters23545912
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcategoria_2
2nd rowcategoria_3
3rd rowcategoria_4
4th rowcategoria_5
5th rowcategoria_6

Common Values

ValueCountFrequency (%)
categoria_3 459528
21.9%
categoria_7 435661
20.7%
categoria_5 288953
13.8%
categoria_11 156428
 
7.4%
categoria_1 149293
 
7.1%
categoria_12 132750
 
6.3%
categoria_8 119130
 
5.7%
categoria_9 80231
 
3.8%
categoria_10 53115
 
2.5%
categoria_6 49806
 
2.4%
Other values (17) 174941
 
8.3%

Length

2025-03-21T19:40:45.145279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
categoria_3 459528
21.9%
categoria_7 435661
20.7%
categoria_5 288953
13.8%
categoria_11 156428
 
7.4%
categoria_1 149293
 
7.1%
categoria_12 132750
 
6.3%
categoria_8 119130
 
5.7%
categoria_9 80231
 
3.8%
categoria_10 53115
 
2.5%
categoria_6 49806
 
2.4%
Other values (17) 174941
 
8.3%

Most occurring characters

ValueCountFrequency (%)
a 4199672
17.8%
c 2099836
8.9%
t 2099836
8.9%
e 2099836
8.9%
g 2099836
8.9%
o 2099836
8.9%
r 2099836
8.9%
i 2099836
8.9%
_ 2099836
8.9%
1 733652
 
3.1%
Other values (9) 1813900
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18898524
80.3%
Decimal Number 2547552
 
10.8%
Connector Punctuation 2099836
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 733652
28.8%
3 473505
18.6%
7 440274
17.3%
5 297406
11.7%
2 211316
 
8.3%
8 119549
 
4.7%
6 92463
 
3.6%
9 80905
 
3.2%
0 60335
 
2.4%
4 38147
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
a 4199672
22.2%
c 2099836
11.1%
t 2099836
11.1%
e 2099836
11.1%
g 2099836
11.1%
o 2099836
11.1%
r 2099836
11.1%
i 2099836
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2099836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18898524
80.3%
Common 4647388
 
19.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2099836
45.2%
1 733652
 
15.8%
3 473505
 
10.2%
7 440274
 
9.5%
5 297406
 
6.4%
2 211316
 
4.5%
8 119549
 
2.6%
6 92463
 
2.0%
9 80905
 
1.7%
0 60335
 
1.3%
Latin
ValueCountFrequency (%)
a 4199672
22.2%
c 2099836
11.1%
t 2099836
11.1%
e 2099836
11.1%
g 2099836
11.1%
o 2099836
11.1%
r 2099836
11.1%
i 2099836
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23545912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4199672
17.8%
c 2099836
8.9%
t 2099836
8.9%
e 2099836
8.9%
g 2099836
8.9%
o 2099836
8.9%
r 2099836
8.9%
i 2099836
8.9%
_ 2099836
8.9%
1 733652
 
3.1%
Other values (9) 1813900
7.7%
Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size143.0 MiB
2025-03-21T19:40:45.276123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.432911
Min length14

Characters and Unicode

Total characters30306747
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowsubcategoria_2
2nd rowsubcategoria_3
3rd rowsubcategoria_4
4th rowsubcategoria_5
5th rowsubcategoria_6
ValueCountFrequency (%)
subcategoria_5 663877
31.6%
subcategoria_3 241198
 
11.5%
subcategoria_9 151660
 
7.2%
subcategoria_12 74603
 
3.6%
subcategoria_14 73388
 
3.5%
subcategoria_22 66665
 
3.2%
subcategoria_7 61671
 
2.9%
subcategoria_39 52424
 
2.5%
subcategoria_13 50023
 
2.4%
subcategoria_24 46695
 
2.2%
Other values (92) 617632
29.4%
2025-03-21T19:40:45.531249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4199672
13.9%
s 2099836
 
6.9%
g 2099836
 
6.9%
u 2099836
 
6.9%
i 2099836
 
6.9%
r 2099836
 
6.9%
o 2099836
 
6.9%
_ 2099836
 
6.9%
e 2099836
 
6.9%
t 2099836
 
6.9%
Other values (12) 7208551
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25198032
83.1%
Decimal Number 3008879
 
9.9%
Connector Punctuation 2099836
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4199672
16.7%
s 2099836
8.3%
g 2099836
8.3%
u 2099836
8.3%
i 2099836
8.3%
r 2099836
8.3%
o 2099836
8.3%
e 2099836
8.3%
t 2099836
8.3%
c 2099836
8.3%
Decimal Number
ValueCountFrequency (%)
5 776736
25.8%
3 479481
15.9%
2 458167
15.2%
1 416572
13.8%
4 267289
 
8.9%
9 227900
 
7.6%
7 144637
 
4.8%
0 91787
 
3.1%
6 85964
 
2.9%
8 60346
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2099836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25198032
83.1%
Common 5108715
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4199672
16.7%
s 2099836
8.3%
g 2099836
8.3%
u 2099836
8.3%
i 2099836
8.3%
r 2099836
8.3%
o 2099836
8.3%
e 2099836
8.3%
t 2099836
8.3%
c 2099836
8.3%
Common
ValueCountFrequency (%)
_ 2099836
41.1%
5 776736
 
15.2%
3 479481
 
9.4%
2 458167
 
9.0%
1 416572
 
8.2%
4 267289
 
5.2%
9 227900
 
4.5%
7 144637
 
2.8%
0 91787
 
1.8%
6 85964
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30306747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4199672
13.9%
s 2099836
 
6.9%
g 2099836
 
6.9%
u 2099836
 
6.9%
i 2099836
 
6.9%
r 2099836
 
6.9%
o 2099836
 
6.9%
_ 2099836
 
6.9%
e 2099836
 
6.9%
t 2099836
 
6.9%
Other values (12) 7208551
23.8%
Distinct7280
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size138.1 MiB
2025-03-21T19:40:45.779198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.978037
Min length10

Characters and Unicode

Total characters25151913
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)< 0.1%

Sample

1st rowproducto_2
2nd rowproducto_3
3rd rowproducto_4
4th rowproducto_5
5th rowproducto_6
ValueCountFrequency (%)
producto_49 53825
 
2.6%
producto_19 49097
 
2.3%
producto_72 26394
 
1.3%
producto_176 25418
 
1.2%
producto_40 24688
 
1.2%
producto_3 23448
 
1.1%
producto_119 21339
 
1.0%
producto_28 20676
 
1.0%
producto_110 20628
 
1.0%
producto_67 20391
 
1.0%
Other values (7270) 1813932
86.4%
2025-03-21T19:40:46.042398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4199672
16.7%
p 2099836
8.3%
d 2099836
8.3%
u 2099836
8.3%
c 2099836
8.3%
t 2099836
8.3%
_ 2099836
8.3%
r 2099836
8.3%
1 1117368
 
4.4%
2 753269
 
3.0%
Other values (8) 4382752
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16798688
66.8%
Decimal Number 6253389
 
24.9%
Connector Punctuation 2099836
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1117368
17.9%
2 753269
12.0%
3 732386
11.7%
4 629896
10.1%
7 543362
8.7%
6 541610
8.7%
9 537752
8.6%
5 493316
7.9%
8 461200
7.4%
0 443230
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
o 4199672
25.0%
p 2099836
12.5%
d 2099836
12.5%
u 2099836
12.5%
c 2099836
12.5%
t 2099836
12.5%
r 2099836
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2099836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16798688
66.8%
Common 8353225
33.2%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2099836
25.1%
1 1117368
13.4%
2 753269
 
9.0%
3 732386
 
8.8%
4 629896
 
7.5%
7 543362
 
6.5%
6 541610
 
6.5%
9 537752
 
6.4%
5 493316
 
5.9%
8 461200
 
5.5%
Latin
ValueCountFrequency (%)
o 4199672
25.0%
p 2099836
12.5%
d 2099836
12.5%
u 2099836
12.5%
c 2099836
12.5%
t 2099836
12.5%
r 2099836
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25151913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4199672
16.7%
p 2099836
8.3%
d 2099836
8.3%
u 2099836
8.3%
c 2099836
8.3%
t 2099836
8.3%
_ 2099836
8.3%
r 2099836
8.3%
1 1117368
 
4.4%
2 753269
 
3.0%
Other values (8) 4382752
17.4%

color
Text

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.0 MiB
2025-03-21T19:40:46.156972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.9198956
Min length3

Characters and Unicode

Total characters18730318
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowGRIS
2nd rowBEIGE
3rd rowNo encontrado
4th rowBLANCO
5th rowNo encontrado
ValueCountFrequency (%)
no 984853
31.9%
encontrado 984853
31.9%
gris 424960
13.8%
blanco 242752
 
7.9%
beige 172110
 
5.6%
multicolor 89249
 
2.9%
marfil 43116
 
1.4%
negro 41193
 
1.3%
azul 28952
 
0.9%
mate 14583
 
0.5%
Other values (60) 58068
 
1.9%
2025-03-21T19:40:46.327745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 2954559
15.8%
n 1969706
 
10.5%
N 1289979
 
6.9%
984853
 
5.3%
e 984853
 
5.3%
c 984853
 
5.3%
t 984853
 
5.3%
r 984853
 
5.3%
a 984853
 
5.3%
d 984853
 
5.3%
Other values (26) 5622103
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10833383
57.8%
Uppercase Letter 6912082
36.9%
Space Separator 984853
 
5.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1289979
18.7%
I 760150
11.0%
R 644416
9.3%
G 641863
9.3%
L 534572
7.7%
O 496052
 
7.2%
E 441075
 
6.4%
B 428581
 
6.2%
S 426714
 
6.2%
A 387843
 
5.6%
Other values (17) 860837
12.5%
Lowercase Letter
ValueCountFrequency (%)
o 2954559
27.3%
n 1969706
18.2%
e 984853
 
9.1%
c 984853
 
9.1%
t 984853
 
9.1%
r 984853
 
9.1%
a 984853
 
9.1%
d 984853
 
9.1%
Space Separator
ValueCountFrequency (%)
984853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17745465
94.7%
Common 984853
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2954559
16.6%
n 1969706
11.1%
N 1289979
 
7.3%
e 984853
 
5.5%
c 984853
 
5.5%
t 984853
 
5.5%
r 984853
 
5.5%
a 984853
 
5.5%
d 984853
 
5.5%
I 760150
 
4.3%
Other values (25) 4861953
27.4%
Common
ValueCountFrequency (%)
984853
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18730203
> 99.9%
None 115
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2954559
15.8%
n 1969706
 
10.5%
N 1289979
 
6.9%
984853
 
5.3%
e 984853
 
5.3%
c 984853
 
5.3%
t 984853
 
5.3%
r 984853
 
5.3%
a 984853
 
5.3%
d 984853
 
5.3%
Other values (24) 5621988
30.0%
None
ValueCountFrequency (%)
Ú 77
67.0%
É 38
33.0%

cantidad
Real number (ℝ)

High correlation  Skewed 

Distinct6230
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.054409
Minimum0
Maximum489689
Zeros508
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:46.429066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3.02
Q312.96
95-th percentile149.31
Maximum489689
Range489689
Interquartile range (IQR)11.96

Descriptive statistics

Standard deviation746.40079
Coefficient of variation (CV)19.614043
Kurtosis201638.51
Mean38.054409
Median Absolute Deviation (MAD)2.02
Skewness377.71593
Sum79908017
Variance557114.15
MonotonicityNot monotonic
2025-03-21T19:40:46.528124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 589514
28.1%
2 270560
 
12.9%
5 73652
 
3.5%
4 49233
 
2.3%
25 47632
 
2.3%
3 42377
 
2.0%
10 35155
 
1.7%
50 31807
 
1.5%
6 22941
 
1.1%
3.2 20553
 
1.0%
Other values (6220) 916412
43.6%
ValueCountFrequency (%)
0 508
< 0.1%
0.15 1
 
< 0.1%
0.33 1
 
< 0.1%
0.4 30
 
< 0.1%
0.42 1
 
< 0.1%
0.48 74
 
< 0.1%
0.5 13
 
< 0.1%
0.52 7
 
< 0.1%
0.54 22
 
< 0.1%
0.57 1
 
< 0.1%
ValueCountFrequency (%)
489689 1
< 0.1%
477673 1
< 0.1%
264542 1
< 0.1%
262179 1
< 0.1%
251039 1
< 0.1%
246562 1
< 0.1%
189346 1
< 0.1%
167730 1
< 0.1%
162539 1
< 0.1%
130591 1
< 0.1%

precio
Real number (ℝ)

High correlation  Skewed 

Distinct9634
Distinct (%)0.5%
Missing531
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.2442102
Minimum0
Maximum12043.48
Zeros398
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:46.628168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.65
median2.99
Q36.27
95-th percentile43.33
Maximum12043.48
Range12043.48
Interquartile range (IQR)5.62

Descriptive statistics

Standard deviation29.530505
Coefficient of variation (CV)3.1944866
Kurtosis18669.961
Mean9.2442102
Median Absolute Deviation (MAD)2.39
Skewness75.620122
Sum19406417
Variance872.05074
MonotonicityNot monotonic
2025-03-21T19:40:46.728749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56 42332
 
2.0%
0.5 36596
 
1.7%
0.6 35823
 
1.7%
0.47 33628
 
1.6%
0.65 31075
 
1.5%
0.07 30871
 
1.5%
0.13 24975
 
1.2%
0.08 22515
 
1.1%
0.11 22032
 
1.0%
0.15 21862
 
1.0%
Other values (9624) 1797596
85.6%
ValueCountFrequency (%)
0 398
 
< 0.1%
0.01 2
 
< 0.1%
0.04 373
 
< 0.1%
0.05 60
 
< 0.1%
0.06 1545
 
0.1%
0.07 30871
1.5%
0.08 22515
1.1%
0.09 11929
 
0.6%
0.1 2643
 
0.1%
0.11 22032
1.0%
ValueCountFrequency (%)
12043.48 1
< 0.1%
6451.52 1
< 0.1%
5833.75 1
< 0.1%
5592.58 1
< 0.1%
5591.54 1
< 0.1%
4955.1 1
< 0.1%
4816.04 1
< 0.1%
4099.78 1
< 0.1%
4044.55 1
< 0.1%
3846.93 2
< 0.1%

valor
Real number (ℝ)

High correlation  Skewed 

Distinct46571
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.991693
Minimum0
Maximum56876.09
Zeros872
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 MiB
2025-03-21T19:40:47.014620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.17
Q14.69
median13.32
Q337.13
95-th percentile146.3
Maximum56876.09
Range56876.09
Interquartile range (IQR)32.44

Descriptive statistics

Standard deviation165.07401
Coefficient of variation (CV)4.1277075
Kurtosis18659.162
Mean39.991693
Median Absolute Deviation (MAD)10.83
Skewness86.352678
Sum83975996
Variance27249.429
MonotonicityNot monotonic
2025-03-21T19:40:47.114680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13 33436
 
1.6%
1.2 29827
 
1.4%
2.34 20694
 
1.0%
1.3 19555
 
0.9%
2.49 15704
 
0.7%
1.17 13754
 
0.7%
0.35 10842
 
0.5%
2.5 9731
 
0.5%
2.67 9279
 
0.4%
4.69 8032
 
0.4%
Other values (46561) 1928982
91.9%
ValueCountFrequency (%)
0 872
< 0.1%
0.07 32
 
< 0.1%
0.08 2
 
< 0.1%
0.09 22
 
< 0.1%
0.1 36
 
< 0.1%
0.11 19
 
< 0.1%
0.12 40
 
< 0.1%
0.13 223
 
< 0.1%
0.14 32
 
< 0.1%
0.15 83
 
< 0.1%
ValueCountFrequency (%)
56876.09 1
< 0.1%
55491.7 1
< 0.1%
32647.37 1
< 0.1%
32398.94 1
< 0.1%
30571.52 1
< 0.1%
30508.79 1
< 0.1%
30434.9 1
< 0.1%
29158.44 1
< 0.1%
28599.27 1
< 0.1%
24053.41 1
< 0.1%

alineación con portafolio estratégico
Real number (ℝ)

High correlation  Skewed 

Distinct24169
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9451203
Minimum-24187.462
Maximum4162.2267
Zeros2045
Zeros (%)0.1%
Negative1438
Negative (%)0.1%
Memory size16.0 MiB
2025-03-21T19:40:47.214686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-24187.462
5-th percentile0.12096
Q10.473472
median1.410048
Q33.725568
95-th percentile14.263776
Maximum4162.2267
Range28349.689
Interquartile range (IQR)3.252096

Descriptive statistics

Standard deviation21.921295
Coefficient of variation (CV)5.5565594
Kurtosis708193.35
Mean3.9451203
Median Absolute Deviation (MAD)1.150848
Skewness-623.44251
Sum8284105.7
Variance480.5432
MonotonicityNot monotonic
2025-03-21T19:40:47.309123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.124416 33990
 
1.6%
0.117504 33368
 
1.6%
0.2592 24607
 
1.2%
0.134784 22377
 
1.1%
0.245376 18426
 
0.9%
0.048384 15546
 
0.7%
0.12096 13252
 
0.6%
0.487296 11126
 
0.5%
0.27648 10525
 
0.5%
0.342144 8889
 
0.4%
Other values (24159) 1907730
90.9%
ValueCountFrequency (%)
-24187.46227 1
< 0.1%
-1712.174976 1
< 0.1%
-796.189824 1
< 0.1%
-701.059968 1
< 0.1%
-684.160128 1
< 0.1%
-587.48544 1
< 0.1%
-497.726208 1
< 0.1%
-455.728896 1
< 0.1%
-237.102336 1
< 0.1%
-215.046144 1
< 0.1%
ValueCountFrequency (%)
4162.226688 1
< 0.1%
3887.008128 1
< 0.1%
2969.6544 1
< 0.1%
2900.669184 1
< 0.1%
2229.645312 1
< 0.1%
2158.852608 1
< 0.1%
2016.144 1
< 0.1%
1932.795648 1
< 0.1%
1932.436224 1
< 0.1%
1788.9984 1
< 0.1%

Interactions

2025-03-21T19:40:24.564178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:07.204336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:10.018076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:12.890906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:15.728837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:18.704900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:21.763974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:24.928940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:07.645858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:10.392979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:13.266225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:16.098867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:19.151603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:22.128338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:25.354138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:08.034565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:10.782058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:13.628792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:16.548064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:19.567016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:22.514448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:25.815541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:08.411138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:11.163607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:14.030705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:17.048833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:20.013988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:22.900970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:26.294758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:08.826813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:11.564485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:14.516337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:17.430861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:20.414893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:23.396595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:26.775426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:09.199104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:12.039448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:14.928111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:17.802595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:20.902424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:23.787431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:27.464419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:09.620333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:12.446305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:15.348749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:18.198007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:21.383126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-03-21T19:40:24.163604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2025-03-21T19:40:47.398083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
alineación con portafolio estratégicocantidadcategoriacategoria_macroclusteredadidpedidopreciovalorzona
alineación con portafolio estratégico1.0000.3690.0260.0160.045-0.067-0.058-0.0070.4940.9820.004
cantidad0.3691.0000.0380.0060.066-0.042-0.058-0.011-0.5300.3900.006
categoria0.0260.0381.0001.0000.0910.0390.0240.0150.0620.0180.032
categoria_macro0.0160.0061.0001.0000.0930.0250.0200.0170.0460.0050.059
cluster0.0450.0660.0910.0931.0000.0570.0420.0160.0260.0560.244
edad-0.067-0.0420.0390.0250.0571.0000.061-0.001-0.028-0.0690.085
id-0.058-0.0580.0240.0200.0420.0611.0000.523-0.007-0.0600.037
pedido-0.007-0.0110.0150.0170.016-0.0010.5231.0000.005-0.0060.014
precio0.494-0.5300.0620.0460.026-0.028-0.0070.0051.0000.4840.000
valor0.9820.3900.0180.0050.056-0.069-0.060-0.0060.4841.0000.027
zona0.0040.0060.0320.0590.2440.0850.0370.0140.0000.0271.000

Missing values

2025-03-21T19:40:30.047449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-21T19:40:34.113450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-21T19:40:39.586117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechapedidoidedadmunicipiozonaasesorpunto de ventaclustercategoria_macrocategoriasubcategoriaproductocolorcantidadpreciovaloralineación con portafolio estratégico
01971-04-302252EL CARMEN DE CHUCURISANTANDERasesor_2punto_venta_2cluster_tienda_2categoria_macro_1categoria_2subcategoria_2producto_2GRIS1.0032.8832.882.920320
11971-04-303331VILLANUEVALA GUAJIRAasesor_3punto_venta_2cluster_tienda_2categoria_macro_2categoria_3subcategoria_3producto_3BEIGE2.000.561.130.117504
21971-04-304443VILLANUEVALA GUAJIRAasesor_4punto_venta_2cluster_tienda_2categoria_macro_3categoria_4subcategoria_4producto_4No encontrado1.008.388.381.251072
31971-04-305531VILLANUEVALA GUAJIRAasesor_5punto_venta_3cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_5BLANCO21.142.2747.993.729024
41971-04-306649ARROYOHONDOBOLÍVARasesor_6punto_venta_4cluster_tienda_2categoria_macro_4categoria_6subcategoria_6producto_6No encontrado1.009.969.961.223424
51971-04-306649ARROYOHONDOBOLÍVARasesor_6punto_venta_4cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_7BRILLANTE1.892.795.280.397440
61971-04-307750VILLANUEVALA GUAJIRAasesor_7punto_venta_2cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_8BLANCO17.012.9349.894.434048
71971-04-307750VILLANUEVALA GUAJIRAasesor_7punto_venta_2cluster_tienda_2categoria_macro_2categoria_7subcategoria_5producto_9GRIS4.563.1814.511.289088
81971-04-308857CABRERACUNDINAMARCAasesor_8punto_venta_5cluster_tienda_2categoria_macro_1categoria_1subcategoria_7producto_10BLANCO1.0011.5011.501.195776
91971-04-309950VILLANUEVALA GUAJIRAasesor_2punto_venta_2cluster_tienda_2categoria_macro_2categoria_8subcategoria_8producto_11MULTICOLOR2.003.066.110.857088
fechapedidoidedadmunicipiozonaasesorpunto de ventaclustercategoria_macrocategoriasubcategoriaproductocolorcantidadpreciovaloralineación con portafolio estratégico
20998261972-09-0193393041922129CURITISANTANDERasesor_380punto_venta_10cluster_tienda_3categoria_macro_1categoria_1subcategoria_26producto_410No encontrado1.000.920.920.082944
20998271972-09-0193393141922428CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_9subcategoria_25producto_267No encontrado1.0015.9015.902.374272
20998281972-09-0193393241922546CURITISANTANDERasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_9subcategoria_25producto_2690No encontrado1.0020.4620.463.335040
20998291972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_3200MULTICOLOR21.063.6777.308.035200
20998301972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_5subcategoria_5producto_699GRIS17.013.0451.725.377536
20998311972-09-0193393336806057CURITISANTANDERasesor_219punto_venta_15cluster_tienda_2categoria_macro_2categoria_7subcategoria_5producto_3328MULTICOLOR45.502.73124.0311.021184
20998321972-09-019339347848929CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_4719BLANCO8.643.5630.782.865024
20998331972-09-0193393541527942CASTILLA LA NUEVAMETAasesor_45punto_venta_16cluster_tienda_3categoria_macro_4categoria_10subcategoria_37producto_1414No encontrado1.0033.0433.043.753216
20998341972-09-0193393641922647NATAGAIMATOLIMAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_5subcategoria_5producto_511MARFIL11.523.7543.144.485888
20998351972-09-0193393641922647NATAGAIMATOLIMAasesor_45punto_venta_16cluster_tienda_3categoria_macro_2categoria_7subcategoria_5producto_248No encontrado1.603.185.090.452736